Data Science for Business Decision Making

Chapters

1Foundations of Data Science in Business

2Business Problem Framing and Decision Science

3Data Strategy, Governance, and Ethics

4Data Acquisition and Quality Management

5Exploratory Data Analysis and Business Understanding

6Descriptive Analytics and BI for Operators

7Statistical Thinking for Decision Making

8Experimentation and Causal Inference

9Predictive Modeling for Business Outcomes

Supervised learning overview Train/validation/test splits Cross-validation schemes Feature engineering patterns Tree-based methods Linear and logistic models Gradient boosting approaches Regularization and hyperparameters Calibration and thresholds Imbalanced class strategies Model interpretability tools Partial dependence and SHAP Model performance metrics Error analysis workflows Model documentation practices

10Time Series and Forecasting

11Customer and Marketing Analytics

12Operations, Pricing, and Optimization

13Data Storytelling, Visualization, and Influence

14Productizing Analytics: MLOps and Deployment

15Measuring Impact, ROI, and Change Management

Courses/Data Science for Business Decision Making/Predictive Modeling for Business Outcomes

Predictive Modeling for Business Outcomes

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Build, evaluate, and interpret models aligned to business objectives and constraints.

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Calibration and thresholds

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